{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Logistic Regression with a Neural Network mindset\n",
    "\n",
    "Welcome to your first (required) programming assignment! You will build a logistic regression classifier to recognize  cats. This assignment will step you through how to do this with a Neural Network mindset, and so will also hone your intuitions about deep learning.\n",
    "\n",
    "**Instructions:**\n",
    "- Do not use loops (for/while) in your code, unless the instructions explicitly ask you to do so.\n",
    "\n",
    "**You will learn to:**\n",
    "- Build the general architecture of a learning algorithm, including:\n",
    "    - Initializing parameters\n",
    "    - Calculating the cost function and its gradient\n",
    "    - Using an optimization algorithm (gradient descent) \n",
    "- Gather all three functions above into a main model function, in the right order."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1 - Packages ##\n",
    "\n",
    "First, let's run the cell below to import all the packages that you will need during this assignment. \n",
    "- [numpy](www.numpy.org) is the fundamental package for scientific computing with Python.\n",
    "- [h5py](http://www.h5py.org) is a common package to interact with a dataset that is stored on an H5 file.\n",
    "- [matplotlib](http://matplotlib.org) is a famous library to plot graphs in Python.\n",
    "- [PIL](http://www.pythonware.com/products/pil/) and [scipy](https://www.scipy.org/) are used here to test your model with your own picture at the end."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import h5py\n",
    "import scipy\n",
    "from PIL import Image\n",
    "from scipy import ndimage\n",
    "from lr_utils import load_dataset\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## 2 - Overview of the Problem set ##\n",
    "\n",
    "**Problem Statement**: You are given a dataset (\"data.h5\") containing:\n",
    "    - a training set of m_train images labeled as cat (y=1) or non-cat (y=0)\n",
    "    - a test set of m_test images labeled as cat or non-cat\n",
    "    - each image is of shape (num_px, num_px, 3) where 3 is for the 3 channels (RGB). Thus, each image is square (height = num_px) and (width = num_px).\n",
    "\n",
    "You will build a simple image-recognition algorithm that can correctly classify pictures as cat or non-cat.\n",
    "\n",
    "Let's get more familiar with the dataset. Load the data by running the following code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Loading the data (cat/non-cat)\n",
    "train_set_x_orig, train_set_y, test_set_x_orig, test_set_y, classes = load_dataset()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(209, 64, 64, 3)\n",
      "(1, 209)\n",
      "(50, 64, 64, 3)\n",
      "(1, 50)\n",
      "[b'non-cat' b'cat']\n",
      "(1,)\n",
      "0\n",
      "()\n",
      "b'non-cat'\n"
     ]
    }
   ],
   "source": [
    "print(train_set_x_orig.shape)\n",
    "print(train_set_y.shape)\n",
    "print(test_set_x_orig.shape)\n",
    "print(test_set_y.shape)\n",
    "print(classes)\n",
    "print(train_set_y[:,1].shape)\n",
    "# np.squeeze?\n",
    "index = 1\n",
    "print(np.squeeze(train_set_y[:, index]))\n",
    "print(np.squeeze(train_set_y[:, index]).shape)\n",
    "print(classes[np.squeeze(train_set_y[:, 4])])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We added \"_orig\" at the end of image datasets (train and test) because we are going to preprocess them. After preprocessing, we will end up with train_set_x and test_set_x (the labels train_set_y and test_set_y don't need any preprocessing).\n",
    "\n",
    "Each line of your train_set_x_orig and test_set_x_orig is an array representing an image. You can visualize an example by running the following code. Feel free also to change the `index` value and re-run to see other images. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "y = [0], it's a 'non-cat' picture.\n"
     ]
    },
    {
     "data": {
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LWzCOD99a6/uSzUA8mDAhp+iawVjcMbeEHFEKmAhNGCTNQ7HgNX9HoR2JVjLu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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa6f84834a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Example of a picture\n",
    "index = 4\n",
    "plt.imshow(train_set_x_orig[index])\n",
    "print (\"y = \" + str(train_set_y[:,index]) + \", it's a '\" + classes[np.squeeze(train_set_y[:, index])].decode(\"utf-8\") +  \"' picture.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Many software bugs in deep learning come from having matrix/vector dimensions that don't fit. If you can keep your matrix/vector dimensions straight you will go a long way toward eliminating many bugs. \n",
    "\n",
    "**Exercise:** Find the values for:\n",
    "    - m_train (number of training examples)\n",
    "    - m_test (number of test examples)\n",
    "    - num_px (= height = width of a training image)\n",
    "Remember that `train_set_x_orig` is a numpy-array of shape (m_train, num_px, num_px, 3). For instance, you can access `m_train` by writing `train_set_x_orig.shape[0]`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of training examples: m_train = 209\n",
      "Number of testing examples: m_test = 50\n",
      "Height/Width of each image: num_px = 64\n",
      "Each image is of size: (64, 64, 3)\n",
      "train_set_x shape: (209, 64, 64, 3)\n",
      "train_set_y shape: (1, 209)\n",
      "test_set_x shape: (50, 64, 64, 3)\n",
      "test_set_y shape: (1, 50)\n"
     ]
    }
   ],
   "source": [
    "### START CODE HERE ### (≈ 3 lines of code)\n",
    "m_train = train_set_x_orig.shape[0]\n",
    "m_test = test_set_x_orig.shape[0]\n",
    "num_px = train_set_x_orig.shape[1]\n",
    "### END CODE HERE ###\n",
    "\n",
    "print (\"Number of training examples: m_train = \" + str(m_train))\n",
    "print (\"Number of testing examples: m_test = \" + str(m_test))\n",
    "print (\"Height/Width of each image: num_px = \" + str(num_px))\n",
    "print (\"Each image is of size: (\" + str(num_px) + \", \" + str(num_px) + \", 3)\")\n",
    "print (\"train_set_x shape: \" + str(train_set_x_orig.shape))\n",
    "print (\"train_set_y shape: \" + str(train_set_y.shape))\n",
    "print (\"test_set_x shape: \" + str(test_set_x_orig.shape))\n",
    "print (\"test_set_y shape: \" + str(test_set_y.shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output for m_train, m_test and num_px**: \n",
    "<table style=\"width:15%\">\n",
    "  <tr>\n",
    "    <td>**m_train**</td>\n",
    "    <td> 209 </td> \n",
    "  </tr>\n",
    "  \n",
    "  <tr>\n",
    "    <td>**m_test**</td>\n",
    "    <td> 50 </td> \n",
    "  </tr>\n",
    "  \n",
    "  <tr>\n",
    "    <td>**num_px**</td>\n",
    "    <td> 64 </td> \n",
    "  </tr>\n",
    "  \n",
    "</table>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For convenience, you should now reshape images of shape (num_px, num_px, 3) in a numpy-array of shape (num_px $*$ num_px $*$ 3, 1). After this, our training (and test) dataset is a numpy-array where each column represents a flattened image. There should be m_train (respectively m_test) columns.\n",
    "\n",
    "**Exercise:** Reshape the training and test data sets so that images of size (num_px, num_px, 3) are flattened into single vectors of shape (num\\_px $*$ num\\_px $*$ 3, 1).\n",
    "\n",
    "A trick when you want to flatten a matrix X of shape (a,b,c,d) to a matrix X_flatten of shape (b$*$c$*$d, a) is to use: \n",
    "```python\n",
    "X_flatten = X.reshape(X.shape[0], -1).T      # X.T is the transpose of X, -1表示程序帮你把维度推断出来\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# np.reshape?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train_set_x_flatten shape: (12288, 209)\n",
      "train_set_y shape: (1, 209)\n",
      "test_set_x_flatten shape: (12288, 50)\n",
      "test_set_y shape: (1, 50)\n",
      "sanity check after reshaping: [17 31 56 22 33]\n"
     ]
    }
   ],
   "source": [
    "# Reshape the training and test examples\n",
    "\n",
    "### START CODE HERE ### (≈ 2 lines of code)\n",
    "train_set_x_flatten = train_set_x_orig.reshape(train_set_x_orig.shape[0], -1).T\n",
    "test_set_x_flatten = test_set_x_orig.reshape(test_set_x_orig.shape[0], -1).T\n",
    "### END CODE HERE ###\n",
    "\n",
    "print (\"train_set_x_flatten shape: \" + str(train_set_x_flatten.shape))\n",
    "print (\"train_set_y shape: \" + str(train_set_y.shape))\n",
    "print (\"test_set_x_flatten shape: \" + str(test_set_x_flatten.shape))\n",
    "print (\"test_set_y shape: \" + str(test_set_y.shape))\n",
    "print (\"sanity check after reshaping: \" + str(train_set_x_flatten[0:5,0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output**: \n",
    "\n",
    "<table style=\"width:35%\">\n",
    "  <tr>\n",
    "    <td>**train_set_x_flatten shape**</td>\n",
    "    <td> (12288, 209)</td> \n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td>**train_set_y shape**</td>\n",
    "    <td>(1, 209)</td> \n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td>**test_set_x_flatten shape**</td>\n",
    "    <td>(12288, 50)</td> \n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td>**test_set_y shape**</td>\n",
    "    <td>(1, 50)</td> \n",
    "  </tr>\n",
    "  <tr>\n",
    "  <td>**sanity check after reshaping**</td>\n",
    "  <td>[17 31 56 22 33]</td> \n",
    "  </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To represent color images, the red, green and blue channels (RGB) must be specified for each pixel, and so the pixel value is actually a vector of three numbers ranging from 0 to 255.\n",
    "\n",
    "One common preprocessing step in machine learning is to center and standardize your dataset, meaning that you substract the mean of the whole numpy array from each example, and then divide each example by the standard deviation of the whole numpy array. But for picture datasets, it is simpler and more convenient and works almost as well to just divide every row of the dataset by 255 (the maximum value of a pixel channel).\n",
    "\n",
    "<!-- During the training of your model, you're going to multiply weights and add biases to some initial inputs in order to observe neuron activations. Then you backpropogate with the gradients to train the model. But, it is extremely important for each feature to have a similar range such that our gradients don't explode. You will see that more in detail later in the lectures. !--> \n",
    "\n",
    "Let's standardize our dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "train_set_x = train_set_x_flatten/255.\n",
    "test_set_x = test_set_x_flatten/255."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color='blue'>\n",
    "**What you need to remember:**\n",
    "\n",
    "Common steps for pre-processing a new dataset are:\n",
    "- Figure out the dimensions and shapes of the problem (m_train, m_test, num_px, ...)\n",
    "- Reshape the datasets such that each example is now a vector of size (num_px \\* num_px \\* 3, 1)\n",
    "- \"Standardize\" the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3 - General Architecture of the learning algorithm ##\n",
    "\n",
    "It's time to design a simple algorithm to distinguish cat images from non-cat images.\n",
    "\n",
    "You will build a Logistic Regression, using a Neural Network mindset. The following Figure explains why **Logistic Regression is actually a very simple Neural Network!**\n",
    "\n",
    "<img src=\"images/LogReg_kiank.png\" style=\"width:650px;height:400px;\">\n",
    "\n",
    "**Mathematical expression of the algorithm**:\n",
    "\n",
    "For one example $x^{(i)}$:\n",
    "$$z^{(i)} = w^T x^{(i)} + b \\tag{1}$$\n",
    "$$\\hat{y}^{(i)} = a^{(i)} = sigmoid(z^{(i)})\\tag{2}$$ \n",
    "$$ \\mathcal{L}(a^{(i)}, y^{(i)}) =  - y^{(i)}  \\log(a^{(i)}) - (1-y^{(i)} )  \\log(1-a^{(i)})\\tag{3}$$\n",
    "\n",
    "The cost is then computed by summing over all training examples:\n",
    "$$ J = \\frac{1}{m} \\sum_{i=1}^m \\mathcal{L}(a^{(i)}, y^{(i)})\\tag{6}$$\n",
    "\n",
    "**Key steps**:\n",
    "In this exercise, you will carry out the following steps: \n",
    "    - Initialize the parameters of the model\n",
    "    - Learn the parameters for the model by minimizing the cost  \n",
    "    - Use the learned parameters to make predictions (on the test set)\n",
    "    - Analyse the results and conclude"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4 - Building the parts of our algorithm ## \n",
    "\n",
    "The main steps for building a Neural Network are:\n",
    "1. Define the model structure (such as number of input features) \n",
    "2. Initialize the model's parameters\n",
    "3. Loop:\n",
    "    - Calculate current loss (forward propagation)\n",
    "    - Calculate current gradient (backward propagation)\n",
    "    - Update parameters (gradient descent)\n",
    "\n",
    "You often build 1-3 separately and integrate them into one function we call `model()`.\n",
    "\n",
    "### 4.1 - Helper functions\n",
    "\n",
    "**Exercise**: Using your code from \"Python Basics\", implement `sigmoid()`. As you've seen in the figure above, you need to compute $sigmoid( w^T x + b) = \\frac{1}{1 + e^{-(w^T x + b)}}$ to make predictions. Use np.exp()."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# GRADED FUNCTION: sigmoid\n",
    "\n",
    "def sigmoid(z):\n",
    "    \"\"\"\n",
    "    Compute the sigmoid of z\n",
    "\n",
    "    Arguments:\n",
    "    z -- A scalar or numpy array of any size.\n",
    "\n",
    "    Return:\n",
    "    s -- sigmoid(z)\n",
    "    \"\"\"\n",
    "\n",
    "    ### START CODE HERE ### (≈ 1 line of code)\n",
    "    s = 1 / (1 + np.exp(-z))\n",
    "    ### END CODE HERE ###\n",
    "    \n",
    "    return s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sigmoid([0, 2]) = [ 0.5         0.88079708]\n"
     ]
    }
   ],
   "source": [
    "print (\"sigmoid([0, 2]) = \" + str(sigmoid(np.array([0,2]))))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output**: \n",
    "\n",
    "<table>\n",
    "  <tr>\n",
    "    <td>**sigmoid([0, 2])**</td>\n",
    "    <td> [ 0.5         0.88079708]</td> \n",
    "  </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.2 - Initializing parameters\n",
    "\n",
    "**Exercise:** Implement parameter initialization in the cell below. You have to initialize w as a vector of zeros. If you don't know what numpy function to use, look up np.zeros() in the Numpy library's documentation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2, 1)\n"
     ]
    }
   ],
   "source": [
    "# np.zeros?\n",
    "dim = 2\n",
    "w = np.zeros((dim, 1))\n",
    "print(w.shape)\n",
    "b = 0\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# GRADED FUNCTION: initialize_with_zeros\n",
    "\n",
    "def initialize_with_zeros(dim):\n",
    "    \"\"\"\n",
    "    This function creates a vector of zeros of shape (dim, 1) for w and initializes b to 0.\n",
    "    \n",
    "    Argument:\n",
    "    dim -- size of the w vector we want (or number of parameters in this case)\n",
    "    \n",
    "    Returns:\n",
    "    w -- initialized vector of shape (dim, 1)\n",
    "    b -- initialized scalar (corresponds to the bias)\n",
    "    \"\"\"\n",
    "    \n",
    "    ### START CODE HERE ### (≈ 1 line of code)\n",
    "    w = np.zeros((dim, 1))\n",
    "    b = 0\n",
    "    ### END CODE HERE ###\n",
    "\n",
    "    assert(w.shape == (dim, 1))\n",
    "    assert(isinstance(b, float) or isinstance(b, int))\n",
    "    \n",
    "    return w, b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "w = [[ 0.]\n",
      " [ 0.]]\n",
      "b = 0\n"
     ]
    }
   ],
   "source": [
    "dim = 2\n",
    "w, b = initialize_with_zeros(dim)\n",
    "print (\"w = \" + str(w))\n",
    "print (\"b = \" + str(b))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output**: \n",
    "\n",
    "\n",
    "<table style=\"width:15%\">\n",
    "    <tr>\n",
    "        <td>  ** w **  </td>\n",
    "        <td> [[ 0.]\n",
    " [ 0.]] </td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td>  ** b **  </td>\n",
    "        <td> 0 </td>\n",
    "    </tr>\n",
    "</table>\n",
    "\n",
    "For image inputs, w will be of shape (num_px $\\times$ num_px $\\times$ 3, 1)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.3 - Forward and Backward propagation\n",
    "\n",
    "Now that your parameters are initialized, you can do the \"forward\" and \"backward\" propagation steps for learning the parameters.\n",
    "\n",
    "**Exercise:** Implement a function `propagate()` that computes the cost function and its gradient.\n",
    "\n",
    "**Hints**:\n",
    "\n",
    "Forward Propagation:\n",
    "- You get X\n",
    "- You compute $A = \\sigma(w^T X + b) = (a^{(0)}, a^{(1)}, ..., a^{(m-1)}, a^{(m)})$\n",
    "- You calculate the cost function: $J = -\\frac{1}{m}\\sum_{i=1}^{m}y^{(i)}\\log(a^{(i)})+(1-y^{(i)})\\log(1-a^{(i)})$\n",
    "\n",
    "Here are the two formulas you will be using: \n",
    "\n",
    "$$ \\frac{\\partial J}{\\partial w} = \\frac{1}{m}X(A-Y)^T\\tag{7}$$\n",
    "$$ \\frac{\\partial J}{\\partial b} = \\frac{1}{m} \\sum_{i=1}^m (a^{(i)}-y^{(i)})\\tag{8}$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# GRADED FUNCTION: propagate\n",
    "\n",
    "def propagate(w, b, X, Y):\n",
    "    \"\"\"\n",
    "    Implement the cost function and its gradient for the propagation explained above\n",
    "\n",
    "    Arguments:\n",
    "    w -- weights, a numpy array of size (num_px * num_px * 3, 1)\n",
    "    b -- bias, a scalar\n",
    "    X -- data of size (num_px * num_px * 3, number of examples)\n",
    "    Y -- true \"label\" vector (containing 0 if non-cat, 1 if cat) of size (1, number of examples)\n",
    "\n",
    "    Return:\n",
    "    cost -- negative log-likelihood cost for logistic regression\n",
    "    dw -- gradient of the loss with respect to w, thus same shape as w\n",
    "    db -- gradient of the loss with respect to b, thus same shape as b\n",
    "    \n",
    "    Tips:\n",
    "    - Write your code step by step for the propagation. np.log(), np.dot()\n",
    "    \"\"\"\n",
    "    \n",
    "    m = X.shape[1]\n",
    "    \n",
    "    # FORWARD PROPAGATION (FROM X TO COST)\n",
    "    ### START CODE HERE ### (≈ 2 lines of code)\n",
    "    A = sigmoid(np.dot(w.T, X) + b)      # compute activation, shape = [1, m]\n",
    "    cost = -1 / m * np.sum(np.log(A) * Y + np.log(1 - A) * (1 - Y))          # compute cost\n",
    "    ### END CODE HERE ###\n",
    "    \n",
    "    # BACKWARD PROPAGATION (TO FIND GRAD)\n",
    "    ### START CODE HERE ### (≈ 2 lines of code)\n",
    "    dw = 1 / m * np.dot(X, (A - Y).T) \n",
    "    db = 1 / m * np.sum(A - Y)\n",
    "    ### END CODE HERE ###\n",
    "\n",
    "    assert(dw.shape == w.shape)\n",
    "    assert(db.dtype == float)\n",
    "    cost = np.squeeze(cost)\n",
    "    assert(cost.shape == ())\n",
    "    \n",
    "    grads = {\"dw\": dw,\n",
    "             \"db\": db}\n",
    "    \n",
    "    return grads, cost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dw = [[ 0.99845601]\n",
      " [ 2.39507239]]\n",
      "db = 0.00145557813678\n",
      "cost = 5.80154531939\n"
     ]
    }
   ],
   "source": [
    "w, b, X, Y = np.array([[1.],[2.]]), 2., np.array([[1.,2.,-1.],[3.,4.,-3.2]]), np.array([[1,0,1]])\n",
    "grads, cost = propagate(w, b, X, Y)\n",
    "print (\"dw = \" + str(grads[\"dw\"]))\n",
    "print (\"db = \" + str(grads[\"db\"]))\n",
    "print (\"cost = \" + str(cost))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output**:\n",
    "\n",
    "<table style=\"width:50%\">\n",
    "    <tr>\n",
    "        <td>  ** dw **  </td>\n",
    "      <td> [[ 0.99845601]\n",
    "     [ 2.39507239]]</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td>  ** db **  </td>\n",
    "        <td> 0.00145557813678 </td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td>  ** cost **  </td>\n",
    "        <td> 5.801545319394553 </td>\n",
    "    </tr>\n",
    "\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### d) Optimization\n",
    "- You have initialized your parameters.\n",
    "- You are also able to compute a cost function and its gradient.\n",
    "- Now, you want to update the parameters using gradient descent.\n",
    "\n",
    "**Exercise:** Write down the optimization function. The goal is to learn $w$ and $b$ by minimizing the cost function $J$. For a parameter $\\theta$, the update rule is $ \\theta = \\theta - \\alpha \\text{ } d\\theta$, where $\\alpha$ is the learning rate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# GRADED FUNCTION: optimize\n",
    "\n",
    "def optimize(w, b, X, Y, num_iterations, learning_rate, print_cost = False):\n",
    "    \"\"\"\n",
    "    This function optimizes w and b by running a gradient descent algorithm\n",
    "    \n",
    "    Arguments:\n",
    "    w -- weights, a numpy array of size (num_px * num_px * 3, 1)\n",
    "    b -- bias, a scalar\n",
    "    X -- data of shape (num_px * num_px * 3, number of examples)\n",
    "    Y -- true \"label\" vector (containing 0 if non-cat, 1 if cat), of shape (1, number of examples)\n",
    "    num_iterations -- number of iterations of the optimization loop\n",
    "    learning_rate -- learning rate of the gradient descent update rule\n",
    "    print_cost -- True to print the loss every 100 steps\n",
    "    \n",
    "    Returns:\n",
    "    params -- dictionary containing the weights w and bias b\n",
    "    grads -- dictionary containing the gradients of the weights and bias with respect to the cost function\n",
    "    costs -- list of all the costs computed during the optimization, this will be used to plot the learning curve.\n",
    "    \n",
    "    Tips:\n",
    "    You basically need to write down two steps and iterate through them:\n",
    "        1) Calculate the cost and the gradient for the current parameters. Use propagate().\n",
    "        2) Update the parameters using gradient descent rule for w and b.\n",
    "    \"\"\"\n",
    "    \n",
    "    costs = []\n",
    "    \n",
    "    for i in range(num_iterations):\n",
    "        \n",
    "        \n",
    "        # Cost and gradient calculation (≈ 1-4 lines of code)\n",
    "        ### START CODE HERE ### \n",
    "        grads, cost = propagate(w, b, X, Y)\n",
    "        ### END CODE HERE ###\n",
    "        \n",
    "        # Retrieve derivatives from grads\n",
    "        dw = grads[\"dw\"]\n",
    "        db = grads[\"db\"]\n",
    "        \n",
    "        # update rule (≈ 2 lines of code)\n",
    "        ### START CODE HERE ###\n",
    "        w = w - learning_rate * dw\n",
    "        b = b - learning_rate * db\n",
    "        ### END CODE HERE ###\n",
    "        \n",
    "        # Record the costs\n",
    "        if i % 100 == 0:\n",
    "            costs.append(cost)\n",
    "        \n",
    "        # Print the cost every 100 training examples\n",
    "        if print_cost and i % 100 == 0:\n",
    "            print (\"Cost after iteration %i: %f\" %(i, cost))\n",
    "    \n",
    "    params = {\"w\": w,\n",
    "              \"b\": b}\n",
    "    \n",
    "    grads = {\"dw\": dw,\n",
    "             \"db\": db}\n",
    "    \n",
    "    return params, grads, costs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "w = [[ 0.19033591]\n",
      " [ 0.12259159]]\n",
      "b = 1.92535983008\n",
      "dw = [[ 0.67752042]\n",
      " [ 1.41625495]]\n",
      "db = 0.219194504541\n"
     ]
    }
   ],
   "source": [
    "params, grads, costs = optimize(w, b, X, Y, num_iterations= 100, learning_rate = 0.009, print_cost = False)\n",
    "\n",
    "print (\"w = \" + str(params[\"w\"]))\n",
    "print (\"b = \" + str(params[\"b\"]))\n",
    "print (\"dw = \" + str(grads[\"dw\"]))\n",
    "print (\"db = \" + str(grads[\"db\"]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output**: \n",
    "\n",
    "<table style=\"width:40%\">\n",
    "    <tr>\n",
    "       <td> **w** </td>\n",
    "       <td>[[ 0.19033591]\n",
    " [ 0.12259159]] </td>\n",
    "    </tr>\n",
    "    \n",
    "    <tr>\n",
    "       <td> **b** </td>\n",
    "       <td> 1.92535983008 </td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "       <td> **dw** </td>\n",
    "       <td> [[ 0.67752042]\n",
    " [ 1.41625495]] </td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "       <td> **db** </td>\n",
    "       <td> 0.219194504541 </td>\n",
    "    </tr>\n",
    "\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise:** The previous function will output the learned w and b. We are able to use w and b to predict the labels for a dataset X. Implement the `predict()` function. There is two steps to computing predictions:\n",
    "\n",
    "1. Calculate $\\hat{Y} = A = \\sigma(w^T X + b)$\n",
    "\n",
    "2. Convert the entries of a into 0 (if activation <= 0.5) or 1 (if activation > 0.5), stores the predictions in a vector `Y_prediction`. If you wish, you can use an `if`/`else` statement in a `for` loop (though there is also a way to vectorize this). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# GRADED FUNCTION: predict\n",
    "\n",
    "def predict(w, b, X):\n",
    "    '''\n",
    "    Predict whether the label is 0 or 1 using learned logistic regression parameters (w, b)\n",
    "    \n",
    "    Arguments:\n",
    "    w -- weights, a numpy array of size (num_px * num_px * 3, 1)\n",
    "    b -- bias, a scalar\n",
    "    X -- data of size (num_px * num_px * 3, number of examples)\n",
    "    \n",
    "    Returns:\n",
    "    Y_prediction -- a numpy array (vector) containing all predictions (0/1) for the examples in X\n",
    "    '''\n",
    "    \n",
    "    m = X.shape[1]\n",
    "    Y_prediction = np.zeros((1,m))\n",
    "    w = w.reshape(X.shape[0], 1)\n",
    "    \n",
    "    # Compute vector \"A\" predicting the probabilities of a cat being present in the picture\n",
    "    ### START CODE HERE ### (≈ 1 line of code)\n",
    "    A = sigmoid(np.dot(w.T, X) + b)\n",
    "    ### END CODE HERE ###\n",
    "    \n",
    "    for i in range(A.shape[1]):\n",
    "        \n",
    "        # Convert probabilities A[0,i] to actual predictions p[0,i]\n",
    "        ### START CODE HERE ### (≈ 4 lines of code)\n",
    "        Y_prediction = np.where(A >= 0.5, 1, 0)\n",
    "        ### END CODE HERE ###\n",
    "#     print(A)\n",
    "    assert(Y_prediction.shape == (1, m))\n",
    "    \n",
    "    return Y_prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "predictions = [[1 1 0]]\n"
     ]
    }
   ],
   "source": [
    "w = np.array([[0.1124579],[0.23106775]])\n",
    "b = -0.3\n",
    "X = np.array([[1.,-1.1,-3.2],[1.2,2.,0.1]])\n",
    "print (\"predictions = \" + str(predict(w, b, X)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output**: \n",
    "\n",
    "<table style=\"width:30%\">\n",
    "    <tr>\n",
    "         <td>\n",
    "             **predictions**\n",
    "         </td>\n",
    "          <td>\n",
    "            [[ 1.  1.  0.]]\n",
    "         </td>  \n",
    "   </tr>\n",
    "\n",
    "</table>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "<font color='blue'>\n",
    "**What to remember:**\n",
    "You've implemented several functions that:\n",
    "- Initialize (w,b)\n",
    "- Optimize the loss iteratively to learn parameters (w,b):\n",
    "    - computing the cost and its gradient \n",
    "    - updating the parameters using gradient descent\n",
    "- Use the learned (w,b) to predict the labels for a given set of examples"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5 - Merge all functions into a model ##\n",
    "\n",
    "You will now see how the overall model is structured by putting together all the building blocks (functions implemented in the previous parts) together, in the right order.\n",
    "\n",
    "**Exercise:** Implement the model function. Use the following notation:\n",
    "    - Y_prediction for your predictions on the test set\n",
    "    - Y_prediction_train for your predictions on the train set\n",
    "    - w, costs, grads for the outputs of optimize()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# GRADED FUNCTION: model\n",
    "\n",
    "def model(X_train, Y_train, X_test, Y_test, num_iterations = 2000, learning_rate = 0.5, print_cost = False):\n",
    "    \"\"\"\n",
    "    Builds the logistic regression model by calling the function you've implemented previously\n",
    "    \n",
    "    Arguments:\n",
    "    X_train -- training set represented by a numpy array of shape (num_px * num_px * 3, m_train)\n",
    "    Y_train -- training labels represented by a numpy array (vector) of shape (1, m_train)\n",
    "    X_test -- test set represented by a numpy array of shape (num_px * num_px * 3, m_test)\n",
    "    Y_test -- test labels represented by a numpy array (vector) of shape (1, m_test)\n",
    "    num_iterations -- hyperparameter representing the number of iterations to optimize the parameters\n",
    "    learning_rate -- hyperparameter representing the learning rate used in the update rule of optimize()\n",
    "    print_cost -- Set to true to print the cost every 100 iterations\n",
    "    \n",
    "    Returns:\n",
    "    d -- dictionary containing information about the model.\n",
    "    \"\"\"\n",
    "    \n",
    "    ### START CODE HERE ###\n",
    "    nx = X_train.shape[0]\n",
    "    # initialize parameters with zeros (≈ 1 line of code)\n",
    "    w, b = initialize_with_zeros(nx)\n",
    "\n",
    "    # Gradient descent (≈ 1 line of code)\n",
    "    parameters, grads, costs = optimize(w, b, X_train, Y_train, num_iterations, learning_rate, print_cost)\n",
    "    \n",
    "    # Retrieve parameters w and b from dictionary \"parameters\"\n",
    "    w = parameters[\"w\"]\n",
    "    b = parameters[\"b\"]\n",
    "    \n",
    "    # Predict test/train set examples (≈ 2 lines of code)\n",
    "    Y_prediction_test = predict(w, b, X_test)\n",
    "    Y_prediction_train = predict(w, b, X_train)\n",
    "\n",
    "    ### END CODE HERE ###\n",
    "\n",
    "    # Print train/test Errors\n",
    "    print(\"train accuracy: {} %\".format(100 - np.mean(np.abs(Y_prediction_train - Y_train)) * 100))\n",
    "    print(\"test accuracy: {} %\".format(100 - np.mean(np.abs(Y_prediction_test - Y_test)) * 100))\n",
    "\n",
    "    \n",
    "    d = {\"costs\": costs,\n",
    "         \"Y_prediction_test\": Y_prediction_test, \n",
    "         \"Y_prediction_train\" : Y_prediction_train, \n",
    "         \"w\" : w, \n",
    "         \"b\" : b,\n",
    "         \"learning_rate\" : learning_rate,\n",
    "         \"num_iterations\": num_iterations}\n",
    "    \n",
    "    return d"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run the following cell to train your model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cost after iteration 0: 0.693147\n",
      "Cost after iteration 100: 0.584508\n",
      "Cost after iteration 200: 0.466949\n",
      "Cost after iteration 300: 0.376007\n",
      "Cost after iteration 400: 0.331463\n",
      "Cost after iteration 500: 0.303273\n",
      "Cost after iteration 600: 0.279880\n",
      "Cost after iteration 700: 0.260042\n",
      "Cost after iteration 800: 0.242941\n",
      "Cost after iteration 900: 0.228004\n",
      "Cost after iteration 1000: 0.214820\n",
      "Cost after iteration 1100: 0.203078\n",
      "Cost after iteration 1200: 0.192544\n",
      "Cost after iteration 1300: 0.183033\n",
      "Cost after iteration 1400: 0.174399\n",
      "Cost after iteration 1500: 0.166521\n",
      "Cost after iteration 1600: 0.159305\n",
      "Cost after iteration 1700: 0.152667\n",
      "Cost after iteration 1800: 0.146542\n",
      "Cost after iteration 1900: 0.140872\n",
      "train accuracy: 99.04306220095694 %\n",
      "test accuracy: 70.0 %\n"
     ]
    }
   ],
   "source": [
    "d = model(train_set_x, train_set_y, test_set_x, test_set_y, num_iterations = 2000, learning_rate = 0.005, print_cost = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output**: \n",
    "\n",
    "<table style=\"width:40%\"> \n",
    "\n",
    "    <tr>\n",
    "        <td> **Cost after iteration 0 **  </td> \n",
    "        <td> 0.693147 </td>\n",
    "    </tr>\n",
    "      <tr>\n",
    "        <td> <center> $\\vdots$ </center> </td> \n",
    "        <td> <center> $\\vdots$ </center> </td> \n",
    "    </tr>  \n",
    "    <tr>\n",
    "        <td> **Train Accuracy**  </td> \n",
    "        <td> 99.04306220095694 % </td>\n",
    "    </tr>\n",
    "\n",
    "    <tr>\n",
    "        <td>**Test Accuracy** </td> \n",
    "        <td> 70.0 % </td>\n",
    "    </tr>\n",
    "</table> \n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "**Comment**: Training accuracy is close to 100%. This is a good sanity check: your model is working and has high enough capacity to fit the training data. Test error is 68%. It is actually not bad for this simple model, given the small dataset we used and that logistic regression is a linear classifier. But no worries, you'll build an even better classifier next week!\n",
    "\n",
    "Also, you see that the model is clearly overfitting the training data. Later in this specialization you will learn how to reduce overfitting, for example by using regularization. Using the code below (and changing the `index` variable) you can look at predictions on pictures of the test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "y = 1, you predicted that it is a \"cat\" picture.\n"
     ]
    },
    {
     "data": {
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zhgYDhm2nkwyY5vXTt9XVIXvsp/LeebSIvU/suKwX7JcV63o5gKgAwXijbFs1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cVkuoAxEOtnBWwdyCE96Vv018DUfkM0pqfUeWFj4uzXiAVFv5Ox8LdsFAdN2Nt1AZxiShgjZ69A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\n8FnOcpaz3KGcQfgsZznLWe5Q/hVcokhRgy2SdQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa6f844ea20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Example of a picture that was wrongly classified.\n",
    "index = 1\n",
    "plt.imshow(test_set_x[:,index].reshape((num_px, num_px, 3)))\n",
    "print (\"y = \" + str(test_set_y[0,index]) + \", you predicted that it is a \\\"\" + classes[d[\"Y_prediction_test\"][0,index]].decode(\"utf-8\") +  \"\\\" picture.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's also plot the cost function and the gradients."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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4VENhIeyyizqZioiI1JWCRzUNGwYTJsDHH8ddiYiISP5S8KimY4+FDTcMfT1ERESkdhQ8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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa6f84424e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot learning curve (with costs)\n",
    "costs = np.squeeze(d['costs'])\n",
    "plt.plot(costs)\n",
    "plt.ylabel('cost')\n",
    "plt.xlabel('iterations (per hundreds)')\n",
    "plt.title(\"Learning rate =\" + str(d[\"learning_rate\"]))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "**Interpretation**:\n",
    "You can see the cost decreasing. It shows that the parameters are being learned. However, you see that you could train the model even more on the training set. Try to increase the number of iterations in the cell above and rerun the cells. You might see that the training set accuracy goes up, but the test set accuracy goes down. This is called overfitting. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6 - Further analysis (optional/ungraded exercise) ##\n",
    "\n",
    "Congratulations on building your first image classification model. Let's analyze it further, and examine possible choices for the learning rate $\\alpha$. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Choice of learning rate ####\n",
    "\n",
    "**Reminder**:\n",
    "In order for Gradient Descent to work you must choose the learning rate wisely. The learning rate $\\alpha$  determines how rapidly we update the parameters. If the learning rate is too large we may \"overshoot\" the optimal value. Similarly, if it is too small we will need too many iterations to converge to the best values. That's why it is crucial to use a well-tuned learning rate.\n",
    "\n",
    "Let's compare the learning curve of our model with several choices of learning rates. Run the cell below. This should take about 1 minute. Feel free also to try different values than the three we have initialized the `learning_rates` variable to contain, and see what happens. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "learning rate is: 0.02\n",
      "train accuracy: 100.0 %\n",
      "test accuracy: 68.0 %\n",
      "\n",
      "-------------------------------------------------------\n",
      "\n",
      "learning rate is: 0.01\n",
      "train accuracy: 99.52153110047847 %\n",
      "test accuracy: 68.0 %\n",
      "\n",
      "-------------------------------------------------------\n",
      "\n",
      "learning rate is: 0.001\n",
      "train accuracy: 88.99521531100478 %\n",
      "test accuracy: 64.0 %\n",
      "\n",
      "-------------------------------------------------------\n",
      "\n",
      "learning rate is: 0.0001\n",
      "train accuracy: 68.42105263157895 %\n",
      "test accuracy: 36.0 %\n",
      "\n",
      "-------------------------------------------------------\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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2800+Otq31wnmmS2lpZCZ6b/EA+Duu6FdO3jqKf9dU3hm7NixLFq0iKFDhzJ9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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa6d14222e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learning_rates = [0.02, 0.01, 0.001, 0.0001]\n",
    "models = {}\n",
    "for i in learning_rates:\n",
    "    print (\"learning rate is: \" + str(i))\n",
    "    models[str(i)] = model(train_set_x, train_set_y, test_set_x, test_set_y, num_iterations = 1500, learning_rate = i, print_cost = False)\n",
    "    print ('\\n' + \"-------------------------------------------------------\" + '\\n')\n",
    "\n",
    "for i in learning_rates:\n",
    "    plt.plot(np.squeeze(models[str(i)][\"costs\"]), label= str(models[str(i)][\"learning_rate\"]))\n",
    "\n",
    "plt.ylabel('cost')\n",
    "plt.xlabel('iterations')\n",
    "\n",
    "legend = plt.legend(loc='upper center', shadow=True)\n",
    "frame = legend.get_frame()\n",
    "frame.set_facecolor('0.90')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Interpretation**: \n",
    "- Different learning rates give different costs and thus different predictions results.\n",
    "- If the learning rate is too large (0.01), the cost may oscillate up and down. It may even diverge (though in this example, using 0.01 still eventually ends up at a good value for the cost). \n",
    "- A lower cost doesn't mean a better model. You have to check if there is possibly overfitting. It happens when the training accuracy is a lot higher than the test accuracy.\n",
    "- In deep learning, we usually recommend that you: \n",
    "    - Choose the learning rate that better minimizes the cost function.\n",
    "    - If your model overfits, use other techniques to reduce overfitting. (We'll talk about this in later videos.) \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## 7 - Test with your own image (optional/ungraded exercise) ##\n",
    "\n",
    "Congratulations on finishing this assignment. You can use your own image and see the output of your model. To do that:\n",
    "    1. Click on \"File\" in the upper bar of this notebook, then click \"Open\" to go on your Coursera Hub.\n",
    "    2. Add your image to this Jupyter Notebook's directory, in the \"images\" folder\n",
    "    3. Change your image's name in the following code\n",
    "    4. Run the code and check if the algorithm is right (1 = cat, 0 = non-cat)!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "64\n"
     ]
    }
   ],
   "source": [
    "scipy.misc.imresize?\n",
    "print(num_px)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "y = 0, your algorithm predicts a \"non-cat\" picture.\n"
     ]
    },
    {
     "data": {
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roO6rUtGxfu8/MkTdPZ+6ZVZbtz3vdy2/wzSg3hbY7d+jVZBQkO8mQ9FWMTJvriUM7WDl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\nxZTHAuwF1H8OgN9S2DoA/G8ANyLy8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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa6d7b4cc18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## START CODE HERE ## (PUT YOUR IMAGE NAME) \n",
    "my_image = \"mycat.jpg\"   # change this to the name of your image file \n",
    "## END CODE HERE ##\n",
    "\n",
    "# We preprocess the image to fit your algorithm.\n",
    "fname = \"images/\" + my_image\n",
    "image = np.array(ndimage.imread(fname, flatten=False))\n",
    "my_image = scipy.misc.imresize(image, size=(num_px,num_px)).reshape((1, num_px*num_px*3)).T   # imresize会自动调整图片大小为指定像素，我们的model只能支持常数维（64*64,num_px=64）的图片\n",
    "my_predicted_image = predict(d[\"w\"], d[\"b\"], my_image)\n",
    "\n",
    "plt.imshow(image)\n",
    "print(\"y = \" + str(np.squeeze(my_predicted_image)) + \", your algorithm predicts a \\\"\" + classes[int(np.squeeze(my_predicted_image)),].decode(\"utf-8\") +  \"\\\" picture.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color='blue'>\n",
    "**What to remember from this assignment:**\n",
    "1. Preprocessing the dataset is important.\n",
    "2. You implemented each function separately: initialize(), propagate(), optimize(). Then you built a model().\n",
    "3. Tuning the learning rate (which is an example of a \"hyperparameter\") can make a big difference to the algorithm. You will see more examples of this later in this course!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, if you'd like, we invite you to try different things on this Notebook. Make sure you submit before trying anything. Once you submit, things you can play with include:\n",
    "    - Play with the learning rate and the number of iterations\n",
    "    - Try different initialization methods and compare the results\n",
    "    - Test other preprocessings (center the data, or divide each row by its standard deviation)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bibliography:\n",
    "- http://www.wildml.com/2015/09/implementing-a-neural-network-from-scratch/\n",
    "- https://stats.stackexchange.com/questions/211436/why-do-we-normalize-images-by-subtracting-the-datasets-image-mean-and-not-the-c"
   ]
  }
 ],
 "metadata": {
  "coursera": {
   "course_slug": "neural-networks-deep-learning",
   "graded_item_id": "XaIWT",
   "launcher_item_id": "zAgPl"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
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